Social bookmarking, semantic tagging, and technologies around community centric collaboration are resulting in a tremendous amount of noise and data content. That is, a large volume of data is created through such community centric collaboration, however, the vast majority of the data is often irrelevant to any given user at any given time. As such, a user wishing to extract specific information from the large volume of data must sift through all the irrelevant data, the noise, in order to extract the specific information the user is looking for. In a community setting, such as a social network, such noise presents problems for a broad audience in terms of how much of the data is relevant, and in terms quickly getting to the content that is relevant to them. These problems are expanding in a proportional way to how the body of collaborative data conventionally expands. As the audience collaborating and adding to the content grows, the aspect of data relevance (or irrelevance to any given user at any given time) grows exponentially.
As one example of this problem, individuals who state that they are expert on arbitrary topics may believe they are in fact experts. However in some social circles, or from some viewpoints, the “expert's” input, comments, and additions may be seen as distracting, noise, irrelevant, or even junk. As an extension of this problem, the same metaphor applies to activity centric computing where activities and threads can be matured by a plurality of individuals that make up an unbounded, or even bounded, community. A great many of the activities or threads may be nothing but noise to any given user at any given time. Likewise, in a team room or document library there is a great deal of noise, in terms of information that is not relevant to a given user at a given time. The challenge is to reduce that noise to allow access to relevant information.